A primary immunological feature of Kaposi's Sarcoma (KS) and opportunistic infections (OI) is a severe depression in T4 count. Repeated T4 measurements are commonly used to monitor disease status, but there has been relatively little research concerning the best use of these repeated data for outcome prediction: current prediction rules use a simple cutoff of 200-400 cells mu L. Using previously collected data from the New York Blood Center (NYBC) and the Memorial Sloan-Kettering Cancer Center (MSKCC) (441 homosexual men followed up to 5 years, 69 with AIDS), novel statistical methods will be developed and applied to repeated T4 counts in order to predict KS and OI in HIV-Type I infected individuals. Early and accurate predictions, based on more complete use of repeated T4 data, might help the clinical investigator to delay or to prevent clinically significant immune deficiency in those with AIDS, and to clarify the distinctions between individuals with KS and OI. Initial analysis of the NYBC/MSKCC data shoed that several months in advance of KS or OI, when T4 levels still exceeded conventional diagnostic thresholds, T4 counts began to decline markedly. For further insight into the prognostic value of T4, proposed analysis of these data includes phases of data exploration, model development, and validation. A major task will be to develop and apply to the NYBC/MSKCC data three innovative statistical models: an original one that combines repeated measures and time-to-event data, a non-linear regression for longitudinal data, and an adaptation of the Cox model for time-to-event data.